First of all, I would like to thank all of you for having shown overwhelming interest in articles I have written on the Sarawak State Election. I am certainly encouraged by your response and will certainly continue to write good quality articles based on quantitative analysis of data.

Results of the Sarawak State Election 2011 held yesterday showed that Barisan Nasional has managed to capture more than 2 / 3 of the DUN seats, when it won 55 seats compared with Pakatan Rakyat’s 15 seats. Details are as follows:

The above table shows that Barisan Nasional won 55 seats against 15 won by Pakatan Rakyat, while an independent candidatewon a seat. These results confirm our preelection prediction which we published earlier.

Compared with the 2006 Sarawak state elections, BN had lost eight seats while DAP increased its seats to 12, and PKR 3 seats.

The improved performance of DAP was as expected due to overwhelming support from Chinese voters for the party’s candidates. For PKR, it has failed to achieve victory that they anticipated. In fact, the three seats they won were not because of the party’s popularity. For example, their victory in Padungan is riding on the popularity of the DAP and, in Ba’Kelalan Baru Bian’s victory was largely because of his personal popularity. At Kerian, it was an unexpected victory for PKR arising from personal weaknesses of the BN candidate himself.

Voter support from various racial groups for the National Front is shown in the table below:

The above table has been prepared from result of regression analysis of all data from yesterday’s election. It shows the Malay/ Melanau votes for BN has increased to 81.7% , compared to 77.1% in 2006, an increase of 4.6%.

The support of the Dayak people for BN is still comfortable at 61.2%, a decrease of 6.3% over the figures achieved in the 2006 state election. However after taking into consideration spoilt votes and votes that went to independent candidates, it is estimated that only 34% of the Dayaks voted for Pakatan Rakyat.

Support for BN from the Chinese community plunged to an all time low of only 24.6% compared with 40.4% obtained in the 2006 state election.

Decline in support from the Dayak community is quite significant and this should need urgent attention from the Barisan Nasional, in order to maintain its strength in the next General Election. NCR land issue and the Bible issue played by the opposition are sensitive to the ethnic Dayak.

With regard to the collapse of support from the Chinese community, there is nothing more the Government can do, as it has exhausted all approaches. The Chinese community seems to feel that they gain more by opting for confrontation with the National Front Government and don’t seem to appreciate the efforts of the Prime Minister’s to reach out to them.

There was an air of hysteria among the Chinese community in Sarawak during the election campaign. It was hard to figure out what really happened and what they really wanted. Did they really believe that they could topple the BN government in Sarawak? If so, why? Couldn’t they sense that they were alone and the rest of the state followed different path? Perhaps this could have happened only when they only read racist Chinese newspapers and mesmerised by the huge DAP rallies during the campaign period.

Perhaps, it didn’t really matter to the Sarawak Chinese community to track down this lonely path outside the government. This is a big sacrifice they inadvertently took which helps DAP’s cause in Semenanjung Malaysia. This may be the more logical explanation, since the collapse of Chinese support in Sarawak would not make it so easy for Najib to decide on an early election. Should the voting results be more friendly towards BN, Najib would surely not hesitate to call an early general election and destroy the opposition in the process.

The nagging question now is whether the Chinese voting pattern in Sarawak is similar to their counterpart in Semenanjung. If we look back to the 2006 Sarawak State Election, the Chinese support for BN had already dropped to 40% of their total votes. This anti BN sentiment spread to Semenanjung in the 2008 General Election when Chinese support also dropped to exactly the same figure 40%.

Does this mean Chinese support for BN in Semenanjung has also dropped that low to 24.6%? Perhaps yes. Perhaps not, since the the overwhelming issue to the Chinese in Sarawak was the Chief Minister himself, and their belief that the Dayak were with them to topple the state government.

DAP may not be able to duplicate such a scenario in Semenanjung. Moreover, Barisan partners in Semenanjung are different, though not without their own bad image. Still Chinese support for BN in Semenanjung is not as bad if we gauge by the results of the last five byelections.

The Sarawak State Election outcome also indicates that the Malay and Dayak confidence and trust in the BN is still strong. However, to sustain this confidence and to perpetuate Malay and Bumiputera unity it is important that UMNO and Barisan Nasional continue to champion Malay and Bumiputera interest.

PKR failed miserably in Sarawak, especially in the Malay/Melanau areas. Support from this racial group for BN is the highest, ie 81.7%, meaning that PKR only managed to collect not more than 17% of their votes. In fact in Sarawak Anwar is more and more seen as a Chinese leader judging by the hysteria he created at huge Pakatan Rakyat rallies which were virtually attended by the Chinese community only.

PAS has been more realistic. They kept a low profile and away from Anwar. In fact they only competed in a handful of seats and almost won in Beting Maro where they lost by a mere 391 votes. Only the sheer might and popularity of Najib that managed to keep this constituency inside BN.

Tuan,
“In fact, the three seats they won were not because of the party’s popularity. For example, their victory in Padungan is riding on the popularity of the DAP and, in Ba’Kelalan Baru Bian’s victory was largely because of his personal popularity.”

The PKR won in Batu Lintang, a predominantly Chinese 83% constituency.

I have read your analysis and I think it is fair as an attempt to interpret figures objectively. In fact, your analysis is more reliable and more objective than one given by Bridget Welsh in Malaysiakini. Welsh’s, in spite of her credential, is more like a writing to advance Pakatan Rakyat’s cause.

My articles on Sarawak Election is widely read on the internet. I have developed loyal followers by being objective and factual. My analysis is always based on regression analysis of complete data of the election results. Such analysis can come out with a mean figure for the entire state for BN or Pakatan Rakyat on the basis of support by Malay/Melanau, Chinese and Dayak races individually. However, this method of analysis needs skill in statistics discipline which is beyond most of the political analysts.

In short the source of my analysis is the actual electoral and results data covering the DUN Sarawak constituencies. The finding is purely my own based on the above analysis.

Did the Chinese vote for DAP really love DAP? Or they just got fed up with SUPP who had not groomed up the young like DAP? Shouldn’t SUPP be blamed for their complacency rather than on the popularity of the chief minister? Urban voters are unlike the ruler voters in their needs n demands. This scenario is similar in West Malaysia. BN will always be strong in the rural area because they have the financial clout to push for infrastructure development.
The last election when BN lost the support of the urban voters will indirectly beneftit the Rural voters. Federal BN or Najib will have to allocate money for the rural if they still want to

Saudara Juan,
It may be corrrect to say that some Chinese voters are fed up of SUPP and some don’t like the Chief minister for overstaying. Howerver, I feel the issue is more than that.
Chinese voters are more united now, not only in Sarawak but also in Semenanjung as mentioned in my article above. The only reason to explain this is racial sentiment, ie Chinese see more advantage uniting under DAP than under BN component parties because of DAP’s strong position in Pakatan Rakyat. However, the Malays are rallying behind BN for this very same racial sentiment.
Sooner or late the Chinese will realise that they will get nowhere by supporting DAP as they will be out of the State as well as Federal Cabinet. The Chinese community take their participation in the government for granted, but once out they will realise what they will miss.
The issue of different attitude of rural vs urban voters is also overplayed. The Malay/Melanau voters also live in town areas, eg there are four Malay constituencies covering Kuching and suburbs, and all are stronghols of BN.

1. For the regression, did you transform the variables? Without such transformations, results can be spurious, as the nature of the variables on both sides of the equation do not meet the necessary assumptions for regression, assuming you used linear regression.

For instance, ethnic proportions individually vary from 0 to 100 (if expressed in percentage) or from 0 to 1 (if as a proportion), but the sum across all ethnic proportions cannot exceed 100 or 1 (depending on whether one uses percentage or proportion). So, if one regresses BN proportion of votes on the various ethnic proportions, there is a serious problem in the interpretation of the coefficients. The standard interpretation is that the coefficient is the percentage points or proportion change in BN proportion of votes for each unit change in the associated regressor, keeping all other regressors constant. But that’s the problem with compositional data, you can’t keep all other regressors in the composition constant, as they must sum up to 100 or 1.

2. The percentage support you suggest can be used to generate so-called “predicted” values, and they can be compared to the actual observed values. While it might be comforting for BN to believe that it had some 82% of Malay and Melanau votes, a look at the scattergram of actual vs predicted shows that considerable caution needs to be exercised about this.

3. Ethnicity may well be a proxy for some other variables — economic condition, levels of employment or unemployment, urban/rural, etc. In other words, while we in this country tend to look at everything through ethnic lenses, that may very well not be the determinant of behaviour, including voter behaviour. It would be good, e.g., to use 2010 census data — when it finally appears — to map onto constituencies to have some idea of occupational distribution, education distribution, telephony coverage, computer ownership, etc. and to possibly draw on other data such as road density, etc. to be included in a full-fledged analysis of elections.

Finally, a general comment: while you claim yours to be an objective analysis, your writing, even more your response to freddie, suggests a strong BN slant. For instance, the claim that the NEP has benefitted all bumiputera cannot account for the observation in the recent MDG 2010 report that other bumiputera now account for over half of all poor households in the country, although other bumiputera only account for 12% of all households in the country. Similarly, if one takes the 2000 census and look at educational attainment, it is clear that other bumiputera have fallen behind, relative to all others. Unfortunately, the often-heard claim by other bumiputera that they are 2nd class bumiputera (or 3rd class citizens, if Chinese are taken into account) has considerable support from the facts.

Dear PK,
Thank you for your comments. Indeed this is the first comment I receive from someone having knowledge of multiple regression analysis. While I agree with you that the regression coefficient in this case must fall between 0 and 1 or 0 and 100, it is not correct to say that all coefficients must add up to 1 or 100. In this particular situation it means 81% of the Malays on the electoral register voted for BN, 24% of the chinese and 62% of the Dayak. It can’t add up to 1 or 100 because it is not supposed to. I agree it must add up to 1 or 100 if these racial votes are expressed as percentages of total BN vote.

The reliability of the equation is indicated by R squared value and t values of each coefficient, which in this case are statistically significant.

The equation reliability can also be checked if you use it to compute estimated value of BN votes in each constituency and total number of seats won. If you get an accurate estimation against actual, you cant be that wrong.

In fact using the Sarawak model equation which I derived, the estimated seats won by Pakatan Rakyat was 13 seats in total compared with the actual 15 they obtained. Similar accuracy were also obtained in estimating the results of the Malaysian Parliamentary elections in 2008 and 2004.

Sorry, but it is _not_ the coefficients that are constrained, it’s the variables, i.e., if you use Malay-Melanau (MM), Chinese (C) and Dayak (D), whether as percentages or as proportions, to characterise a constituency, then the constrain is that MM+C+D = 100 (or 1, if proportions).

That’s where the problems come in when you regress BN% against MM, C and D. If you do BN% against MM, C and D, what exactly does the coefficient of MM or C or D mean in this context, since the standard interpretation cannot hold — and note that the standard interpretation of the estimated coefficients is a rate of change per unit change in the variable, holding other variables constant (if you write out the regression equation, then differentiate it, i.e., get dy/dx(i), the meaning of the coefficients become obvious). But that’s the problem: you can’t hold the other variables constant in this instance because of the sum constraint. What some people do is to transform the explanatory variables by taking the log-ratio, in this instance, using, say, C as the “deleted”/”reference” variable, then the log-ratio of MM to C and the log-ratio of D to C, and then regressing the BN% (or better, the logit of the BN%) against the two log-ratios — but interpretation gets a little hairy, and one has to transform the coefficients back.

There is also a problem if you use MM, C and D in the equation, as can be seen from the correlation of estimates — they are, as expected, highly correlated with each other and with the intercept. If you ran the usual regression, i.e., with intercept/constant, I am almost certain the t-test on the MM, C and D coefficients are not significant, although the R-sq may be quite high, i.e., the model, although defective, does explain quite a bit more of the variation in the results than the simple mean BN percentage does. But, in these circumstances, it’s not clear what the R-sq means, nor what the t-tests mean. Looking at it from another angle, basically, the problem is that the MM, C and D variables are not independent of each other.

As you don’t say what you did, you may well have run the regression forcing the intercept to be 0, perhaps thinking that if there’s nobody in the constituency than there would be 0% for BN. That would be illegitimate — and there are plenty of warnings in statistics books about running regressions forcing the intercept to be 0. And if the software you used gave you an R-sq of over 0.9 with a regression like this, be suspicious. (I would junk the software, esp if it doesn’t tell you what it’s doing — it may be comparing the model against one in which y=0 is used as the estimator for the BN%! Better software will not report an R-sq, or else warns you, or gives you an option of testing the model against using the mean as the estimator.)

As for predicted vs actual, if you haven’t done it, I suggest you generate a new variable, predBN, by summing up MM *0.81, C*0.24 and D*0.61, and then do a scatter plot of observed BN (the actual results) vs predBN, and then do a 45-degree line (the line of perfect match) and see how the points are distributed relative to that 45-degree line. Basically, I suspect you obtained the 0.81, 0.24 and 0.61 by summing up the intercept/constant and the respective coefficients, or else simply read them off the regression if you had forced the intercept to be 0.

You could be right (though I don’t think so) about the 0.81, etc., but it would be wrong to think that it’s the regression that justifies it. It doesn’t.

Saudara pk,
Probably you have not understood my formula correctly. The X values that I use are the number of Malay, Chinese and Indian votes cast respectively in each constituency. These are estimated from the racial composition in each constituency and the voter turnout. Y value is the votes that BN obtained. Regressing these values I got the regression coefficient b for each race, which is interpreted as the mean value for each race voting for BN. For instance an increase of 100 MM will result in an increase of (.81*100) in BN votes.In this formula, there is no need to satisfy the condition that MM+C+I=1 or 100, because the variables are not expressed as percentage of votes that BN obtained. What is relevant is BN=bMM+bC+bI to conform to the general equation Y=bX(1)+bX(2)+bX(3)+e

As you expected, I did force a zero intercept. This is perfectly natural for this particular equation since if all values of MM,C and I are zero, BN value is also naturally zero. I do not prefer to use a constant as it will lead to awakward result if all X values are small or zero.

Finally, t values are high with values ranging from 13 to 28, ie highly significant for all the regression coefficients, besides also suggesting the absence of multicollinearity.

The R squared value is also reasonably high at .77 making this equation a fairly accurate estimator.

Briefly, using counts instead of proportions does not negate the problem. In every constituency, MM+C+D = Total Valid Votes, i.e., MM, C or D are not free to vary independently. The problem of compositions still exists.

OK, you are entitled to your opinions, but please take note that I did not use the formula MM+C+D=Total votes as you must have thought. Constraints you have mentioned may exist if using this formula. On the other hand, my formula as mentioned before is MM+C+D=BN. The raw data on the left hand side is not equal to the one on the right, but coefficients derived in the regression makes it equal.

I understand your points, but we have to go back to the original purpose of this regression analysis, ie. to obtain the mean percentage of votes for the various races in the Sarawak state election election. I have run the estimated BN votes in each constituency based on this equation, with the outcome close to the actual result. It should be good enough as a basis for estimating future elections.

If it cannot be used to find the effect of change of each variable on the BN votes, so be it, because it is not the intention in the first place.

As to the issue of multicollinearity, I have cross checked the data and found no significant collinearity in the independent variables. As a matter of fact the T test values for Malay votes is 28.5, for Chinese votes is 13.0 and for Dayaks votes 25.6, indicating highly significant results free from multicollinearity problems.I suggest you try testing it yourself.

Finally, without going too deeply into the test methodology, you can easily draw up a test of say ten hypothetical constituencies with a predetermined mean of racial votes. By applying the formula on the input data against the precalculated results, you will find that the resulting regression equation will confirm the predetermined mean for each race.